class_docstr: This class implements a model-specific automatic speech recognizer using the end-to-end speech recognizer
  DeepSpeech and PyTorch. It supports both version 2 and version 3 of DeepSpeech models as released at
  https://github.com/SeanNaren/deepspeech.pytorch.
  
  | Paper link: https://arxiv.org/abs/1512.02595
docstr: Perform prediction for a batch of inputs.
  
  :param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
            could have different lengths. A possible example of `x` could be:
            `x = np.array([np.array([0.1, 0.2, 0.1, 0.4]), np.array([0.3, 0.1])])`.
  :param batch_size: Batch size.
  :param transcription_output: Indicate whether the function will produce probability or transcription as
                               prediction output. If transcription_output is not available, then probability
                               output is returned. Default: True
  :return: Predicted probability (if transcription_output False) or transcription (default, if
           transcription_output is True):
           - Probability return is a tuple of (probs, sizes), where `probs` is the probability of characters of
           shape (nb_samples, seq_length, nb_classes) and `sizes` is the real sequence length of shape
           (nb_samples,).
           - Transcription return is a numpy array of characters. A possible example of a transcription return
           is `np.array(['SIXTY ONE', 'HELLO'])`.
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code: def predict(
      self, x: np.ndarray, batch_size: int = 128, **kwargs
  ) -> Union[Tuple[np.ndarray, np.ndarray], np.ndarray]:
      """
      Perform prediction for a batch of inputs.
  
      :param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
                could have different lengths. A possible example of `x` could be:
                `x = np.array([np.array([0.1, 0.2, 0.1, 0.4]), np.array([0.3, 0.1])])`.
      :param batch_size: Batch size.
      :param transcription_output: Indicate whether the function will produce probability or transcription as
                                   prediction output. If transcription_output is not available, then probability
                                   output is returned. Default: True
      :return: Predicted probability (if transcription_output False) or transcription (default, if
               transcription_output is True):
               - Probability return is a tuple of (probs, sizes), where `probs` is the probability of characters of
               shape (nb_samples, seq_length, nb_classes) and `sizes` is the real sequence length of shape
               (nb_samples,).
               - Transcription return is a numpy array of characters. A possible example of a transcription return
               is `np.array(['SIXTY ONE', 'HELLO'])`.
      """
      import torch
  
      # Apply preprocessing
      x_preprocessed, _ = self._apply_preprocessing(
          x, y=None, fit=False
      )
  
      x_in = np.empty(len(x_preprocessed), dtype=object)
      x_in[:] = list(x_preprocessed)
  
      # Put the model in the eval mode
      self._model.eval()
  
      # Transform x into the model input space
      (
          inputs,
          _,
          input_rates,
          _,
          batch_idx,
      ) = self._transform_model_input(x=x_in)
  
      # Compute real input sizes
      input_sizes = input_rates.mul_(inputs.size()[-1]).int()
  
      # Run prediction with batch processing
      results = []
      result_output_sizes = np.zeros(x_preprocessed.shape[0], dtype=int)
      num_batch = int(np.ceil(len(x_preprocessed) / float(batch_size)))
  
      for m in range(num_batch):
          # Batch indexes
          begin, end = (
              m * batch_size,
              min((m + 1) * batch_size, x_preprocessed.shape[0]),
          )
  
          # Call to DeepSpeech model for prediction
          with torch.no_grad():
              outputs, output_sizes, _ = self._model(
                  inputs[begin:end].to(self._device),
                  input_sizes[begin:end].to(self._device),
              )
  
          results.append(outputs)
          result_output_sizes[begin:end] = (
              output_sizes.detach().cpu().numpy()
          )
  
      # Aggregate results
      result_outputs = np.zeros(
          shape=(
              x_preprocessed.shape[0],
              result_output_sizes.max(),
              results[0].shape[-1],
          ),
          dtype=config.ART_NUMPY_DTYPE,
      )
  
      for m in range(num_batch):
          # Batch indexes
          begin, end = (
              m * batch_size,
              min((m + 1) * batch_size, x_preprocessed.shape[0]),
          )
  
          # Overwrite results
          result_outputs[
              begin:end, : results[m].shape[1], : results[m].shape[-1]
          ] = (results[m].cpu().numpy())
  
      # Rearrange to the original order
      result_output_sizes_ = result_output_sizes.copy()
      result_outputs_ = result_outputs.copy()
      result_output_sizes[batch_idx] = result_output_sizes_
      result_outputs[batch_idx] = result_outputs_
  
      # Check if users want transcription outputs
      transcription_output = kwargs.get("transcription_output", True)
  
      if transcription_output is False:
          return result_outputs, result_output_sizes
  
      # Now users want transcription outputs
      # Compute transcription
      decoded_output, _ = self.decoder.decode(
          torch.tensor(result_outputs, device=self._device),
          torch.tensor(result_output_sizes, device=self._device),
      )
      decoded_output = [do[0] for do in decoded_output]
      decoded_output = np.array(decoded_output)
  
      return decoded_output
  
imports: import logging
  from typing import TYPE_CHECKING, List, Optional, Tuple, Union
  from pkg_resources import packaging
  import numpy
  from art import config
  from art.estimators.pytorch import PyTorchEstimator
  from art.estimators.speech_recognition.speech_recognizer import SpeechRecognizerMixin, PytorchSpeechRecognizerMixin
  from art.utils import get_file
label_desc: Use image node_id 2 for calling the PyTorchDeepSpeech obj's underlying member method code with example usage: obj.predict(x, batch_size) and returns: decoded_output, result_outputs,  result_output_sizes